Biblio

With the development of Internet technology, the attacker gets more and more complex background knowledge, which makes the anonymous model susceptible to background attack. Although the differential privacy model can resist the background attack, it reduces the versatility of the data. In this paper, this paper proposes a differential privacy information publishing algorithm based on clustering anonymity. The algorithm uses the cluster anonymous algorithm based on KD tree to cluster the original data sets and gets anonymous tables by anonymous operation. Finally, the algorithm adds noise to the anonymous table to satisfy the definition of differential privacy. The algorithm is compared with the DCMDP (Density-Based Clustering Mechanism with Differential Privacy, DCMDP) algorithm under different privacy budgets. The experiments show that as the privacy budget increases, the algorithm reduces the information loss by about 80% of the published data.

In this paper, we apply machine learning to distributed private data owned by multiple data owners, entities with access to non-overlapping training datasets. We use noisy, differentially-private gradients to minimize the fitness cost of the machine learning model using stochastic gradient descent. We quantify the quality of the trained model, using the fitness cost, as a function of privacy budget and size of the distributed datasets to capture the trade-off between privacy and utility in machine learning. This way, we can predict the outcome of collaboration among privacy-aware data owners prior to executing potentially computationally-expensive machine learning algorithms. Particularly, we show that the difference between the fitness of the trained machine learning model using differentially-private gradient queries and the fitness of the trained machine model in the absence of any privacy concerns is inversely proportional to the size of the training datasets squared and the privacy budget squared. We successfully validate the performance prediction with the actual performance of the proposed privacy-aware learning algorithms, applied to: financial datasets for determining interest rates of loans using regression; and detecting credit card frauds using support vector machines.

We consider the problem of reinforcing federated learning with formal privacy guarantees. We propose to employ Bayesian differential privacy, a relaxation of differential privacy for similarly distributed data, to provide sharper privacy loss bounds. We adapt the Bayesian privacy accounting method to the federated setting and suggest multiple improvements for more efficient privacy budgeting at different levels. Our experiments show significant advantage over the state-of-the-art differential privacy bounds for federated learning on image classification tasks, including a medical application, bringing the privacy budget below ε = 1 at the client level, and below ε = 0.1 at the instance level. Lower amounts of noise also benefit the model accuracy and reduce the number of communication rounds.

Differential privacy, which is due to its rigorous mathematical proof and strong privacy guarantee, has become a standard for the release of statistics with privacy protection. Recently, a lot of dynamic data publishing algorithms based on differential privacy have been proposed, but most of the algorithms use a native method to allocate the privacy budget. That is, the limited privacy budget is allocated to each time point uniformly, which may result in the privacy budget being unreasonably utilized and reducing the utility of data. In order to make full use of the limited privacy budget in the dynamic data publishing and improve the utility of data publishing, we propose a dynamic data publishing algorithm based on reinforcement learning in this paper. The algorithm consists of two parts: privacy budget allocation and data release. In the privacy budget allocation phase, we combine the idea of reinforcement learning and the changing characteristics of dynamic data, and establish a reinforcement learning model for the allocation of privacy budget. Finally, the algorithm finds a reasonable privacy budget allocation scheme to publish dynamic data. In the data release phase, we also propose a new dynamic data publishing strategy to publish data after the privacy budget is exhausted. Extensive experiments on real datasets demonstrate that our algorithm can allocate the privacy budget reasonably and improve the utility of dynamic data publishing.

Distributed data aggregation via summation (counting) helped us to learn the insights behind the raw data. However, such computing suffered from a high privacy risk of malicious collusion attacks. That is, the colluding adversaries infer a victim's privacy from the gaps between the aggregation outputs and their source data. Among the solutions against such collusion attacks, Distributed Differential Privacy (DDP) shows a significant effect of privacy preservation. Specifically, a DDP scheme guarantees the global differential privacy (the presence or absence of any data curator barely impacts the aggregation outputs) by ensuring local differential privacy at the end of each data curator. To guarantee an overall privacy performance of a distributed data aggregation system against malicious collusion attacks, part of the existing work on such DDP scheme aim to provide an estimated lower bound of privacy budget for the global differential privacy. However, there are two main problems: low data utility from using a large global function sensitivity; unknown privacy guarantee when the aggregation sensitivity of the whole system is less than the sum of the data curator's aggregation sensitivity. To address these problems while ensuring distributed differential privacy, we provide a new lower bound of privacy budget, which works with an unconditional aggregation sensitivity of the whole distributed system. Moreover, we study the performance of our privacy bound in different scenarios of data updates. Both theoretical and experimental evaluations show that our privacy bound offers better global privacy performance than the existing work.